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すべての Skills

35124 Skills

カテゴリでフィルター

すべてのカテゴリドキュメント開発クリエイティブ生産性コミュニケーションインテグレーションオートメーションデータ処理分析セキュリティ
slavochek2 avatar

awesomesimplification-cascades

Find one insight that eliminates multiple components - "if this is true, we don't need X, Y, or Z"

slavochek2Open Source
slavochek2 avatar

awesomeslack-gif-creator

Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, valida

slavochek2Open Source
slavochek2 avatar

awesomesystematic-debugging

Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never

slavochek2Open Source
slavochek2 avatar

awesometest-driven-development-tdd

Write the test first, watch it fail, write minimal code to pass

slavochek2Open Source
slavochek2 avatar

awesometesting-anti-patterns

Never test mock behavior. Never add test-only methods to production classes. Understand dependencies

slavochek2Open Source
slavochek2 avatar

awesometesting-skills-with-subagents

RED-GREEN-REFACTOR for process documentation - baseline without skill, write addressing failures, it

slavochek2Open Source
slavochek2 avatar

awesomeusing-git-worktrees

Create isolated git worktrees with smart directory selection and safety verification

slavochek2Open Source
slavochek2 avatar

awesomeverification-before-completion

Run verification commands and confirm output before claiming success

slavochek2Open Source
slavochek2 avatar

awesomeweb-artifacts-builder

Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern fronten

slavochek2Open Source
slavochek2 avatar

awesomewhen-stuck-problem-solving-dispatch

Dispatch to the right problem-solving technique based on how you're stuck

slavochek2Open Source
slavochek2 avatar

awesomewriting-skills

TDD for process documentation - test with subagents before writing, iterate until bulletproof

slavochek2Open Source
Orchestra Research avatar

awq-quantization

Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLS...

Orchestra ResearchOpen Source
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